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๐Ÿค– AI

Claude, OpenAI, LLM, Ollama, local llm

CEO Interview with Russ Garcia with Menlo Micro
semiwiki.comยท12h
๐ŸชšWoodworking
Escaping the expert sharks and cruising over the hype of GenAI waves
blog.codonomics.comยท15h
๐ŸชšWoodworking
THE BIBITES - Digital Life
thebibites.comยท1hยท
Discuss: Hacker News
๐ŸชšWoodworking
๐Ÿ“„ How to generate dynamic data structures with Apple Foundation Models
justin.searls.coยท2h
๐ŸชšWoodworking
'Australiana' images made by AI are racist and full of tired cliches, researchers say
phys.orgยท1d
๐ŸชšWoodworking
AI model 'personalities' shape the quality of generated code
theregister.comยท4d
๐ŸชšWoodworking
AIโ€™s backyard: A map of the 21st-century gold rush
english.elpais.comยท23h
๐ŸชšWoodworking
My First $500 MRR โ€“ My thoughts and learnings
news.ycombinator.comยท5hยท
Discuss: Hacker News
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Zero-bullshit take on optimizing websites for LLMs
dodov.devยท1dยท
Discuss: r/webdev
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This CEO laid off nearly 80% of his staff because they refused to adopt AI fast enough. 2 years later, he says heโ€™d do it again
fortune.comยท15h
๐ŸชšWoodworking
Contra Dwarkesh on Continual Learning
interconnects.aiยท2d
๐ŸชšWoodworking
Beyond Chatbots: How Multi-Agent AI Systems Are Revolutionizing Software Engineering
dev.toยท9hยท
Discuss: DEV
๐ŸชšWoodworking
Title: Meta's Approach to Superintelligence: Keeping Control with AI Models
dev.toยท1dยท
Discuss: DEV
๐ŸชšWoodworking
If you teach agentic LLMs a few things about the binaries that exist on your system, sometimes they get smarter
reddit.comยท12hยท
Discuss: r/ClaudeAI
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Choosing the Right Stack in 2025. When AI Feels Like It's Taking Over
dev.toยท5hยท
Discuss: DEV
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Secure, Self-Hosted AI Code Review Powered by Ollama
dev.toยท1dยท
Discuss: DEV
๐ŸชšWoodworking
World.exe: From Gods to NPCs ๐Ÿ˜‡๐Ÿค–๐ŸŽ
dev.toยท17hยท
Discuss: DEV
๐ŸชšWoodworking
Dynamic Fractional Order Optimization via Adaptive Lagrangian Relaxation
dev.toยท7hยท
Discuss: DEV
๐ŸชšWoodworking
Curiosity and Craft: What Drives Me to Build
dev.toยท1dยท
Discuss: DEV
๐ŸชšWoodworking
IADGPT: Unified LVLM for Few-Shot Industrial Anomaly Detection, Localization, and Reasoning via In-Context Learning
arxiv.orgยท2d
๐ŸชšWoodworking
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